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LSTM integration of user-defined loss function

I would like to integrate a custom loss function for my LSTM in python. The code shows my approach so far.
How would I best implement the loss function shown in the images? How would I handle the constraint <0?
Thanks for any help!
Code
# Importing the libraries
ep=25 #Epochs
bs=32 #Batch-Size
vs=0.2 #Validation-Split
r=ep+1 #Range
# Importing the training set
dataset_train = pd.read_csv(r'C:\Users\Name\Desktop\Recurrent Neural Networks\JPM_train.csv',delimiter =';')
training_set = dataset_train.iloc[:, 1:2].values
# Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)
# Creating a data structure with 60 timesteps and 1 output
X_train = []
y_train = []
X_val=[]
y_val=[]
for i in range(60, 1516):
X_train.append(training_set_scaled[i-60:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train, X_val, y_val = np.array(X_train), np.array(y_train), np.array(X_val), np.array(y_val)
# Reshaping
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
def custom_loss(y_true, y_pred):
if(#HERE):
loss=(predicted_stock_price-real_stock_price)^2
else:
loss=0
return loss
# Initialising the RNN
model = Sequential()
# Adding the first LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True, input_shape = (X_train.shape[1], 1)))
model.add(Dropout(0.2))
# Adding a second LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True))
model.add(Dropout(0.2))
# Adding a third LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50, return_sequences = True))
model.add(Dropout(0.2))
# Adding a fourth LSTM layer and some Dropout regularisation
model.add(LSTM(units = 50))
model.add(Dropout(0.2))
# Adding the output layer
model.add(Dense(units = 1))
# Compiling the RNN
model.compile(optimizer = 'adam', loss = custom_loss ,metrics=['accuracy'])
# Fitting the RNN to the Training set
history=model.fit(X_train, y_train, epochs = ep, batch_size = bs, validation_split=vs)
# Getting the real stock price of 2017
dataset_test = pd.read_csv(r'C:\Users\Name\Desktop\Recurrent Neural Networks\JPM_test.csv',delimiter =';')
real_stock_price = dataset_test.iloc[:, 1:2].values
dataset_total = pd.concat((dataset_train['Preis'], dataset_test['Preis']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 80):
X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = model.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)
history_dict = history.history
print(history_dict.keys())
accuracy = history_dict['accuracy']
validation_accuracy = history_dict['val_accuracy']
loss = history_dict['loss']
validation_loss = history_dict['val_loss']
gs = gridspec.GridSpec(2, 2)
#plt.tight_layout()
#plt.subplots_adjust(hspace=1.0)
fig = plt.figure(figsize=(16,16))
# Visualising the results
ax = plt.subplot(gs[1, :]) # row 1, span all columns
plt.plot(real_stock_price, color = 'red', label = 'Real Google Stock Price')
plt.plot(predicted_stock_price, color = 'blue', label = 'Predicted Google Stock Price')
plt.title('Google Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('Google Stock Price')
plt.legend()
plt.show()
Only the Custom loss function
def custom_loss(y_true, y_pred):
if(#HERE):
loss=(predicted_stock_price-real_stock_price)^2
else:
loss=0
return loss
Pictures of the targeted loss function
Here is the link to the original text:
https://www.researchgate.net/publication/342094242_Deep_Stock_Predictions

You can use this loss function that calculates the current prediction (t1) minus the previous real_stock_price (t-1) :
def custom_loss(y_true, y_pred):
if((y_true[0]-y_true[1])*(y_pred-y_true[1])):
loss=(y_pred -y_true[0] )^2
else:
loss=0
return loss
I think that the derivatives in the backpropagation will not be affected by this shifting of time.

IndexError: tuple index out of range (LSTM)

I was following a python project to loosely predict the price of stocks when I encountered an index error with an LSTM model. This is the guide I was following and my code roughly matches: Prediction Tutorial. But for ease of access this is my code exactly:
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from matplotlib.pylab import rcParams
rcParams['figure.figsize'] = 20, 10
df = pd.read_csv('HistoricalData_Apple.csv')
df = df[['Date', 'Close/Last']]
df = df.replace({'\$':''}, regex=True)
df = df.astype({"Close/Last": float})
df["Date"] = pd.to_datetime(df.Date, format="%m/%d/%Y")
df.index = df['Date']
df = df.sort_index(ascending=True, axis=0)
data = pd.DataFrame(index=range(0, len(df)), columns=['Date', 'Close/Last'])
for i in range(0, len(data)):
data["Date"][i] = df['Date'][i]
data["Close/Last"][i] = df["Close/Last"][i]
scaler = MinMaxScaler(feature_range=(0, 1))
data.index = data.Date
data.drop("Date", axis=1, inplace=True)
final_data = data.values
train_data = final_data[0:200, :]
valid_data = final_data[200:, :]
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(final_data)
x_train_data, y_train_data = [], []
for i in range(60, len(train_data)):
x_train_data.append(scaled_data[i-60:i, 0])
y_train_data.append(scaled_data[i, 0])
lstm_model = Sequential()
lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=
(np.shape(x_train_data)[1], 1)))
lstm_model.add(LSTM(units=50))
lstm_model.add(Dense(1))
model_data = data[len(data)-len(valid_data)-60:].values
model_data = model_data.reshape(-1, 1)
model_data = scaler.transform(model_data)
lstm_model.compile(loss='mean_squared_error', optimizer='adam')
lstm_model.fit(x_train_data, y_train_data, epochs=1, batch_size=1, verbose=2)
X_test=[]
for i in range(60, model_data.shape[0]):
X_test.append(model_data[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = lstm_model.predict(X_test)
predicted_stock_price = scaler.inverse_transform(predicted_stock_price)
train_data = data[:200]
valid_data = data[200:]
valid_data['Predictions'] = predicted_stock_price
plt.plot(train_data["Close"])
plt.plot(valid_data[['Close', "Predictions"]])
plt.show()
This code should be working according to the tutorial I was following, but every time I run the code I receive this error message:
2021-11-08 14:57:34.659018: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
Traceback (most recent call last): File "C:/Users/ME/AppData/Roaming/JetBrains/PyCharmCE2021.2/scratches/scratch_1.py", line 42, in <module> lstm_model.add(LSTM(units=50, return_sequences=True, input_shape=(np.shape(x_train_data)[1], 1))) IndexError: tuple index out of range
I do not know what this means exactly or how to fix it.

The expected input shape of the LSTM layer is (batch, timesteps, features). As you have only one feature you can reshape your training sequences as follows
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)
as you already did for the validation sequences.
import pandas as pd
import numpy as np
import yfinance as yf
from keras.models import Sequential
from keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler
pd.options.mode.chained_assignment = None
# download the data
df = yf.download(tickers=['AAPL'], period='10y')
# split the data
train_data = df[['Close']].iloc[: - 200, :]
valid_data = df[['Close']].iloc[- 200:, :]
# scale the data
scaler = MinMaxScaler(feature_range=(0, 1))
scaler.fit(train_data)
train_data = scaler.transform(train_data)
valid_data = scaler.transform(valid_data)
# extract the training sequences
x_train, y_train = [], []
for i in range(60, train_data.shape[0]):
x_train.append(train_data[i - 60: i, 0])
y_train.append(train_data[i, 0])
x_train = np.array(x_train)
y_train = np.array(y_train)
# extract the validation sequences
x_valid = []
for i in range(60, valid_data.shape[0]):
x_valid.append(valid_data[i - 60: i, 0])
x_valid = np.array(x_valid)
# reshape the sequences
x_train = x_train.reshape(x_train.shape[0], x_train.shape[1], 1)
x_valid = x_valid.reshape(x_valid.shape[0], x_valid.shape[1], 1)
# train the model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=x_train.shape[1:]))
model.add(LSTM(units=50))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(x_train, y_train, epochs=50, batch_size=128, verbose=1)
# generate the model predictions
y_pred = model.predict(x_valid)
y_pred = scaler.inverse_transform(y_pred)
y_pred = y_pred.flatten()
# plot the model predictions
df.rename(columns={'Close': 'Actual'}, inplace=True)
df['Predicted'] = np.nan
df['Predicted'].iloc[- y_pred.shape[0]:] = y_pred
df[['Actual', 'Predicted']].plot(title='AAPL')

Error after attempting to train simple LSTM with SPY data

I think these errors havev something to do with the format of my data or the way my code is interacting with the data set, but I'm not a developer by any stretch of the imagination so I'm not really sure exactly what is going on.
/Users/kylehammerberg/PycharmProjects/LSTM1P/matplottest.py:54: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray
X_test = np.array(X_test)
Traceback (most recent call last):
File "/Users/kylehammerberg/PycharmProjects/LSTM1P/matplottest.py", line 55, in
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
IndexError: tuple index out of range
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import keras
url = 'https://raw.githubusercontent.com/khammerberg53/MLPROJ1/main/SP500.csv'
dataset_train = pd.read_csv(url)
training_set = dataset_train.iloc[:, 1:2].values
dataset_train.head()
print(dataset_train.head())
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0,1))
training_set_scaled = sc.fit_transform(training_set)
X_train = []
y_train = []
for i in range(60, 2000):
X_train.append(training_set_scaled[i-60:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import Dense
model = Sequential()
model.add(LSTM(units=50,return_sequences=True,input_shape=(X_train.shape[1], 1)))
model.add(Dropout(0.2))
model.add(LSTM(units=50,return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50,return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(units=50))
model.add(Dropout(0.2))
model.add(Dense(units=1))
model.compile(optimizer='adam',loss='mean_squared_error')
model.fit(X_train,y_train,epochs=100,batch_size=32)
url = 'https://raw.githubusercontent.com/khammerberg53/MLPROJ1/main/SP500%20test%20setcsv.csv'
dataset_test = pd.read_csv(url)
real_stock_price = dataset_test.iloc[:, 1:2].values
dataset_total = pd.concat((dataset_train['Value'], dataset_test['Value']), axis = 0)
inputs = dataset_total[len(dataset_total) - len(dataset_test) - 60:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
X_test = []
for i in range(3, 100):
X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
predicted_stock_price = model.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)
plt.plot(real_stock_price, color = 'black', label = 'TATA Stock Price')
plt.plot(predicted_stock_price, color = 'green', label = 'Predicted TATA Stock Price')
plt.title('TATA Stock Price Prediction')
plt.xlabel('Time')
plt.ylabel('TATA Stock Price')
plt.legend()
plt.show()
print(plt.show())

You cannot range from 3 to 100 if you define your X_test the way you do. If you change your code to:
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
X_test = []
for i in range(60, 161):
X_test.append(inputs[i-60:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
the rest of the code will produce (I only took 2 epochs and might explain that the predictions aren't what you expected):
with 20 epochs, you'd get this:

LSTM prediction. How can I start a prediction?

I have a problem with the prediction. The program is designed to predict stock market prices. Here EUR / USD.
I trained the model and tested it with the test data. The results look good. At this point I can't get any further.
How can I make a prediction?
I tested the program with data from yesterday.
How can I predict today's data?
What should I enter and in what format?
import math
import matplotlib.pyplot as plt
import keras
import pandas as pd
import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Dropout
from keras.layers import *
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from keras.callbacks import EarlyStopping
daten=open("C:")
df = pd.read_csv(daten)
df
training_set = df.iloc[436065:438073, 4:5]
test_set = df.iloc[438074:438170, 4:5]
sc = MinMaxScaler(feature_range = (0, 1))
training_set_scaled = sc.fit_transform(training_set)
X_train = []
y_train = []
for i in range(70, 2000):
X_train.append(training_set_scaled[i-70:i, 0])
y_train.append(training_set_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
n=50
dop=0.1
model = Sequential()#1
model.add(LSTM(units = n, return_sequences = True, input_shape = (X_train.shape[1], 1)))
model.add(Dropout(dop))#2
model.add(LSTM(units = n, return_sequences = True))
model.add(Dropout(dop))#3
model.add(LSTM(units = n, return_sequences = True))
model.add(Dropout(dop))#4
model.add(LSTM(units = n, return_sequences = True))
model.add(Dropout(dop))#5
model.add(LSTM(units = n, return_sequences = True))
model.add(Dropout(dop))#6
model.add(LSTM(units = n, return_sequences = True))
model.add(Dropout(dop))#7
model.add(LSTM(units = n))
model.add(Dropout(dop))
model.add(Dense(units = 1))
model.compile(optimizer = 'adam', loss = 'mean_squared_error')
model.fit(X_train, y_train, epochs = 100, batch_size =32)
dataset_total = pd.concat((training_set, test_set), axis = 0)
inputs = dataset_total[len(dataset_total) - len(test_set) - 70:].values
inputs = inputs.reshape(-1,1)
inputs = sc.transform(inputs)
type(inputs.shape)
X_test = []
for i in range(70, 167):
X_test.append(inputs[i-70:i, 0])
X_test = np.array(X_test)
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
print(X_test.shape)
predicted_stock_price = model.predict(X_test)
predicted_stock_price = sc.inverse_transform(predicted_stock_price)
predicted_stock_price.shape
plt.plot(df.loc[438074:438170, "Close"])
plt.grid(True)
plt.plot(predicted_stock_price)
plt.grid(True)

How to match expected input shape to array shape in CNN model?

I am attempting a CNN for classification of biological data (EEG data). However, after importing and splitting the data into train/test/dev sets and building the CNN, I cannot get the input array shape to match the expected array shape.
Note: Data contains 5 trials/samples for each participant(ID's) in the study, so GSS was used to ensure data from each participant was not mixed train and test sets.
Code and error as follows:
#Load Data
def load_all_data(filename):
import numpy as np
a = np.load(filename)
d = (dict(zip(("data1{}".format(k) for k in a), (a[k] for k in a))))
return d
filename = ("dataname.npz")
X = load_all_data(filename)['array_0']
y = load_all_data(filename)['array_1']
IDs = load_all_data(filename)['array_2']
#Split Test Data with Groupshuffle Split
from sklearn.model_selection import GroupShuffleSplit
import numpy as np
test_size = 0.2
gss = GroupShuffleSplit(n_splits = 1, test_size = 0.2)
for train,test in gss.split(X, y, IDs):
X_train = X[train]
y_train = y[train]
IDs_train = IDs[train]
X_test = X[test]
y_test = y[test]
IDs_test = IDs[test]
fileoutname = 'train_test_data'
np.savez(fileoutname,X_train, y_train, X_test, y_test,IDs_train,IDs_test)
#Split Train, Test Data
gss = GroupShuffleSplit(1, test_size)
for train,test in gss.split(X, y, IDs):
X_train2 = X[train]
y_train2 = y[train]
IDs_train = IDs[train]
X_dev = X[test]
y_dev = y[test]
IDs_test = IDs[test]
#Add dimension to X and Convert y to Categorical
X_train2 = np.expand_dims(X_train2,axis=0)
y_train2 = keras.utils.to_categorical(y_train2,num_classes=2)
X_dev = np.expand_dims(X_test,axis=0)
y_dev = keras.utils.to_categorical(y_test,num_classes=2)
X = np.expand_dims(X,axis=0)
#Build the CNN
def simpleCNN(self, units = 10):
import keras
from keras.layers import Dense
from keras.layers import Conv2D
from keras.layers import Flatten
from keras.models import Model, Input
inp = Input(shape = self.shape[1:], name='inp')
#layer 1
x = Conv2D(units, kernel_size=(1,1), strides = (1,1), activation='relu', data_format='channels_last')(inp)
#layer 2
x = Conv2D(units, kernel_size=(2,2), strides = (1,1), activation='relu', data_format='channels_last')(x)
#layer 3
x = Flatten()(x)
#layer4
out = Dense(2, activation='softmax',name='out')(x)
model = Model(inputs = inp, outputs = out)
return model
#Fit the Data
model = simpleCNN(X)
from keras.optimizers import Adamax
adamax = Adamax(lr=3e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0); #3e-4; 2e-3 is a default.
model.compile(optimizer=adamax, loss='categorical_crossentropy', metrics=['acc'])
model.fit(X_train2, y_train2, epochs=20, batch_size=32, verbose = 1, validation_data = (X_dev, y_dev))
ValueError: Error when checking input: expected inp to have shape (11459, 26, 60) but got array with shape (9065, 26, 60)